What is Cost Per Evaluation?

Cost Per Evaluation (CPE) is a foundational financial metric in AI quality assurance. It represents the total expenditure required to complete one evaluation unit, calculated as:

CPE = Total Eval Program Cost / Number of Evaluations Run

This simple formula masks tremendous complexity. "Total cost" includes everything from human rater hourly wages to cloud infrastructure to management overhead. "Evaluations" varies widely—does it count individual predictions, batches, or complete model assessments? Understanding CPE requires breaking the metric into its components and recognizing that naive calculation misleads.

For a team running 10,000 evaluations with a $50,000 budget, the naive CPE appears to be $5.00. But this obscures critical questions: What quality were those evaluations? How many required multiple raters? What was the agreement rate? How much rework was needed? Was infrastructure utilization efficient?

The most useful CPE calculation acknowledges two things: first, that you must distinguish between cost (what you spend) and value (what you get), and second, that CPE varies dramatically depending on evaluation type, complexity, and domain specialization.

$0.003
Average LLM judge cost per evaluation
$25–$150
Typical human rater cost per evaluation
47x
Cost difference between human and LLM judge
3.2%
Typical ML budget allocated to evaluation

Breaking Down Eval Costs

Evaluation budgets consist of five major categories. Understanding each helps identify where waste occurs and where investment yields returns.

1. Human Annotator Labor Costs

Human evaluation remains the gold standard for complex tasks. Costs depend critically on three factors: hourly rate, task complexity (time per evaluation), and quality requirements (single vs. multiple raters).

Hourly rates vary enormously by geography and expertise:

Task complexity dramatically affects labor cost. A straightforward binary classification (thumbs up/thumbs down) takes 30–60 seconds. A nuanced evaluation requiring expertise—assessing whether a legal document summary covers all material facts—requires 5–15 minutes. A medical evaluation requiring clinical judgment might demand 30 minutes or more.

Multiple raters increase cost linearly. Evaluating with three independent raters costs roughly 3x the single-rater cost (before you factor in disagreement resolution, which adds another 5–10%).

2. LLM Judge API Costs

Using LLM-as-Judge (an LLM evaluating another LLM's output) has transformed evaluation economics. Costs depend on model choice and prompt complexity:

The hidden cost in LLM judgment is prompt engineering. A poorly designed prompt might require 20,000 tokens of examples and instructions per evaluation. A well-crafted prompt reduces this to 2,000 tokens, saving 10x on costs. Additionally, LLM judges often require validation against human gold labels—you might run 1,000 evals with LLM judges but validate against 200 human evaluations, adding cost.

3. Infrastructure and Tooling Costs

Platform costs scale differently depending on deployment model:

4. Management and Quality Assurance Overhead

Evaluation programs require management:

5. Analysis and Insights Generation

Raw eval scores are worthless without analysis:

For a typical evaluation program, analysis costs 15–25% of evaluation execution costs. Teams that skip this phase never derive actionable insights from their evaluations.

Human vs. Automated Eval Cost Comparison

The choice between human and automated evaluation is fundamentally about the cost-quality tradeoff. Here's how they compare across multiple dimensions:

Dimension Human Evaluation LLM Judge Hybrid Approach
Cost per Eval $5–$50 $0.001–$0.05 $0.50–$5
Latency 24–72 hours <1 second 1–10 seconds
Consistency Inter-rater agreement 60–85% 100% consistency (same input → same output) High consistency with human oversight
Nuance/Judgment Calls Excellent—humans excel at judgment Variable—depends on prompt design and judge quality Good—LLM flags edge cases, human decides
Scalability Limited by rater availability Unlimited (can run millions instantly) Can scale intelligently
Bias Risk Demographic bias in raters (well-documented) Model biases (often subtle, hard to detect) Mitigated if human review is rigorous
Requires Validation? Not always (humans are trusted) Yes—must validate against human gold labels Yes—spot-check LLM judgments

The cost difference is stark: a single human evaluation at $25 costs what 8,000 LLM-judge evaluations cost. But quality varies dramatically. For tasks where human judgment is essential—evaluating fairness, detecting subtle biases, assessing clinical appropriateness—human evaluation is non-negotiable despite cost. For tasks where objective rules apply—"Does this output contain the word 'apple'?"—LLM judges or even automatic checks are more cost-effective.

Real Cost Calculation Example

Let's walk through a realistic scenario: evaluating a customer support AI model. You plan to run 1,000 evaluations with three independent raters (to ensure quality), then resolve disagreements.

Assumptions

Labor Cost Breakdown

Rater Hours Calculation:

Cost Allocation:

Management and QA Overhead

Tooling and Infrastructure

Analysis

Total Program Cost

$8,000 + $5,500 + $1,500 + $2,640 = $17,640

Cost Per Evaluation

$17,640 ÷ 1,000 evals = $17.64/eval

But notice: if you only count the direct rater labor ($8,000 ÷ 1,000 = $8), you underestimate true cost by 55%. The "invisible" costs—management, QA, tooling, analysis—are critical to evaluation quality and insights.

Industry Reality Check

This $17.64 CPE is actually on the lower end for medium-complexity evaluations with quality oversight. Healthcare, legal, or financial domain evaluations with expert raters often run $50–$200/eval. Simple crowdsourced tasks run $0.50–$2/eval. The key driver isn't task complexity alone—it's the cost of reliable expertise.

Hidden Costs Teams Ignore

Even the detailed calculation above misses costs that accumulate quickly:

Rater Drift Detection and Retraining

Over a 6-month evaluation program, raters gradually drift from standards (their personal calibration changes). Detecting this requires re-evaluating a sample of past cases (~5% of evaluations). This adds 5% overhead that most teams ignore until quality degrades unexpectedly.

Rework Due to Specification Changes

Midway through evaluation, you realize your rubric was ambiguous or incomplete. This requires re-evaluating prior cases. Industry average: 10–15% rework. A 1,000-eval program effectively becomes 1,100–1,150 evals.

Attrition and Replacement

Trained raters leave. Replacing them requires retraining (5–8 hours at $50/hour = $250–$400 per replacement). If you have a 40% annual attrition rate and maintain a pool of 5 raters, that's 2 replacements/year × $350 = $700 annual overhead. Across a 100-person rater pool (large-scale programs), this becomes $14,000+/year.

Tool Implementation and Integration

One-time costs to set up platforms, integrate with your ML pipeline, and build custom workflows. Often $5,000–$20,000 that gets amortized across evaluations but is easily forgotten.

Compliance and Documentation

In regulated domains (healthcare, finance), evaluation documentation requires legal review, bias audits, and formal sign-off. This adds 20–50% overhead.

Iterative Refinement Cycles

Your first evaluation might reveal that your rubric doesn't measure what you thought. Running a "calibration round" of 50–100 evals to refine definitions is common but rarely budgeted in CPE calculations. If you amortize these across future evaluations, it's still real cost.

Common Pitfall

Teams quote "$5/eval" based on rater labor alone, then are shocked when actual program cost hits $15–$20/eval after hidden costs surface. Comprehensive CPE accounting prevents budget surprises and enables better prioritization decisions.

Cost Optimization Strategies

CPE is not fixed. Strategic choices can reduce cost by 40–60% without sacrificing quality if done thoughtfully.

Strategy 1: Tiered Evaluation Pyramid

Not all evaluations require human review. Structure evaluations in tiers:

Cost Math:

You've reduced cost 82% while maintaining quality on critical cases. This is the most impactful optimization.

Strategy 2: Smart Sampling Instead of Full Coverage

Evaluate all outputs for your most critical metric, but sample for secondary metrics:

Statistical theory shows that a 10% random sample provides ±3.1% confidence intervals at 95% confidence for most metrics (sufficient for decision-making). This 50% cost reduction is justified by statistical power analysis.

Strategy 3: Batch Processing and Model Efficiency

If using LLM judges:

These optimizations can reduce LLM judge costs by 40–60% with zero quality loss.

Strategy 4: Crowd Redundancy Reduction

Instead of 3-rater coverage on all items, use adaptive allocation:

Average cost per item: 2.5 raters vs. 3 raters = 17% reduction. Requires adaptive platform capability but pays off at scale.

Strategy 5: Cross-Validation and Gold Label Reduction

Gold labels (human-validated ground truth) are expensive to create but necessary for validation. Instead of creating 10,000 gold labels:

This reduces gold label creation costs by 80–90% while maintaining validation rigor.

ROI of Evaluation Spending

Evaluation is an investment. What's the return? This requires connecting eval spending to business outcomes.

The $50K Eval That Prevented a $2M Disaster

A financial services company developed an AI model for loan approval recommendations. Preliminary internal evals showed 94% accuracy. Before broad deployment, they invested $50,000 in rigorous evaluation:

This evaluation discovered that the model had disparate impact on loan applicants based on zip code (a proxy for race under Fair Housing Act). While overall accuracy was 94%, accuracy for applicants in predominantly minority zip codes was 67%. The model would systematically disadvantage protected groups.

Outcome without evaluation: Deploy, discriminatory loan denials occur, lawsuits filed, regulatory investigation, reputational damage. Estimated cost: $2M+ in settlements, fines, and remediation.

Outcome with evaluation: Issue discovered pre-deployment, model retrained with balanced data, bias testing added to standard process. Cost: $50K investment that prevented $2M loss = 40x ROI.

The $0 Evaluation That Cost $10M

A consumer product company skipped evaluation before deploying a customer support chatbot to production (after basic internal testing). Three weeks later:

Crisis management, customer service escalation, brand recovery campaigns, and lost customer lifetime value totaled ~$10M.

A $30,000 evaluation program (1,000 evals with subject matter experts) would have caught these issues. $30K cost prevented $10M loss = 333x ROI.

Evaluating Your Evaluation ROI

Calculate this way:

Evaluation ROI = (Cost of Prevented Failure - Evaluation Cost) / Evaluation Cost

For high-stakes domains (healthcare, finance, legal), failure costs are enormous. Even a 1-2% reduction in deployment failures justifies large evaluation budgets.

For low-stakes domains (entertainment recommendation, casual chatbots), evaluation budgets should be smaller but still non-zero.

40x
Typical ROI for evaluation preventing regulatory violation
10x
Typical ROI for evaluation preventing customer harm
3x
Typical ROI for general quality improvement

Budgeting Frameworks for Eval

How much should you spend on evaluation? Three frameworks provide guidance:

Framework 1: Percentage of ML Budget

Allocate evaluation as a percentage of your overall ML engineering budget:

Example: A team with $1M annual ML budget allocates 5% = $50K/year to evaluation. This funds roughly 2,500–3,000 moderate-quality evaluations annually or 500–800 high-quality expert evaluations.

Framework 2: Per-Model-Version Approach

Budget based on model release frequency:

If you release 2 major versions/year + 4 standard releases/year + 12 minor updates/year:

Total budget = (2 × $75K) + (4 × $22.5K) + (12 × $3.5K) = $150K + $90K + $42K = $282K/year

Framework 3: Outcome-Based Budgeting

Budget based on business impact of failure:

Spend increases with failure consequence severity, not with engineering difficulty.

Cost Benchmarks by Company Size

What do comparable companies spend? Here are realistic benchmarks:

Startup (Series A, <$5M ARR, <10 ML engineers)

Growth-Stage (Series B–C, $5M–$100M ARR, 20–50 ML engineers)

Mid-Market (Series D+, $100M–$1B ARR, 50–200 ML engineers)

Enterprise (>$1B ARR, 200+ ML engineers, regulatory scrutiny)

These benchmarks reflect that larger organizations have higher absolute spend but often lower cost-per-eval (due to scale economies) and higher per-engineer allocation (evaluation becomes non-optional at scale).

When to Spend More vs. Less

Not all evaluations deserve equal budget. Here's how to prioritize:

Spend More Evaluation Budget When:

Spend Less (But Don't Skip) When:

Decision Matrix

Use this framework:

Risk of Failure Automation Possible Recommended Spend Example Domain
Very High Low $50–$200/eval (expert-heavy) Medical diagnosis, critical bug detection
High Low $15–$50/eval (mixed) Financial decisions, legal analysis
High High $2–$10/eval (automated + spot-check) Code generation, classification
Medium Low $5–$20/eval (human) Customer support quality, content moderation
Medium High $0.50–$3/eval (automated) Recommendation filtering, search ranking
Low High $0.01–$0.50/eval (automated only) Content suggestions, ads ranking
Best Practice

Set evaluation budgets before model development, not after. This forces trade-off thinking: Will you sacrifice some model sophistication to fund better evaluation? Or build the fancier model and accept higher deployment risk? Making this decision upfront prevents underfunded evaluation programs.